Semiconductor devices such as logic and memory devices are typically fabricated by a sequence of processing steps applied to a specimen. The various features and multiple structural levels of the semiconductor devices are formed by these processing steps. For example, lithography among others is one semiconductor fabrication process that involves generating a pattern on a semiconductor wafer. Additional examples of semiconductor fabrication processes include, but are not limited to, chemical-mechanical polishing, etch, deposition, and ion implantation. Multiple semiconductor devices may be fabricated on a single semiconductor wafer and then separated into individual semiconductor devices.
Metrology processes are used at various steps during a semiconductor manufacturing process to detect defects on wafers to promote higher yield. Optical metrology techniques offer the potential for high throughput without the risk of sample destruction. A number of optical metrology based techniques including scatterometry and reflectometry implementations and associated analysis algorithms are commonly used to characterize critical dimensions, film thicknesses, composition and other parameters of nanoscale structures.
Traditionally, optical metrology is performed on targets consisting of thin films and/or repeated periodic structures. During device fabrication, these films and periodic structures typically represent the actual device geometry and material structure or an intermediate design. As devices (e.g., logic and memory devices) move toward smaller nanometer-scale dimensions, characterization becomes more difficult. Devices incorporating complex three-dimensional geometry and materials with diverse physical properties contribute to characterization difficulty.
For example, modern memory structures are often high-aspect ratio, three-dimensional structures that make it difficult for optical radiation to penetrate to the bottom layers. In addition, the increasing number of parameters required to characterize complex structures (e.g., FinFETs), leads to increasing parameter correlation. As a result, the measurement model parameters characterizing the target often cannot be reliably decoupled.
In response to these challenges, more complex optical tools and signal processing computer algorithms have been developed. Measurements are performed over large ranges of several machine parameters (e.g., wavelength, azimuth and angle of incidence, etc.), and often simultaneously. As a result, the measurement time, computation time, and the overall time to generate reliable results, including measurement recipes, increases significantly.
In general, optical metrology techniques applicable to semiconductor structures are indirect methods of measuring physical properties of a metrology target. In most cases, the measured signals cannot be used to directly determine the physical properties of interest. Traditionally, the measurement process consists of formulating a metrology-based target model that attempts to predict the measured signals based on a model of the interaction of the measurement target with the particular metrology system. The metrology-based target model includes a parameterization of the structure in terms of the physical properties of the measurement target of interest (e.g., film thicknesses, critical dimensions, refractive indices, grating pitch, etc.). In addition, the metrology-based target model includes a parameterization of the measurement tool itself (e.g., wavelengths, angles of incidence, polarization angles, etc.).
System parameters are parameters used to characterize the metrology tool itself. Exemplary system parameters include angle of incidence (AOI), analyzer angle (A0), polarizer angle (P0), illumination wavelength, numerical aperture (NA), etc. Target parameters are parameters used to characterize the geometric and material properties of the metrology target. For a thin film specimen, exemplary target parameters include refractive index (or dielectric function tensor), nominal layer thickness of all layers, layer sequence, etc.
Traditionally, a metrology target is provided by a semiconductor device manufacturer. A metrology-based target model is constructed to simulate the geometry and materials of the metrology target and the interaction of the metrology target with one or more metrology systems, or subsystems. A measurement recipe is developed based on an analysis of simulated measurement signals derived from one or more metrology-based target models, each representative of an interaction between the metrology target and a candidate metrology system, or subsystem (e.g., spectroscopic ellipsometers, etc.).
Traditionally, the formulation of the measurement recipe is guided by a sensitivity analysis of the simulated measurement signals. Some examples include an analysis of the derivatives of the simulated measurement signals (e.g., optical signals such as reflectivity) with respect to the target parameters of interest, analysis of parameter correlations, and a prediction of measurement precision in the presence of random temporal noise. The most common approach to assess and optimize metrology systems is based on a 1st order perturbation approach. In this approach, normally distributed random noise affecting the measured signal is translated into an uncertainty of the parameters measured by the metrology system. The estimated parameter uncertainty resulting from the random noise (i.e., measurement parameter precision) is typically used as the main figure of merit for metrology system performance and recipe optimization. This estimate of measurement system precision is typically expressed as a three-sigma value (i.e., a value that is three times the standard deviation of the estimated distribution of parameter values). Optimization and development of a measurement recipe is typically targeted toward improving the expected measurement precision. Some examples are described by J. Ferns et al., in U.S. Patent Publication No. 2012/0022836, “Method for Automated Determination of an Optimally Parameterized Scatterometry Model,” the subject matter of which is incorporated herein by reference in its entirety. Other examples are described by R. Silver et al., in “Fundamental Limits of Optical Critical Dimension Metrology: A Simulation Study,” published in the Proc. of SPIE, Vol. 6518, 65180U, (2007), the subject matter of which is incorporated herein by reference in its entirety.
However, the emphasis on measurement precision as the main figure of merit for optimization limits the effectiveness of the resulting measurement recipe. Recent improvements in light sources, detectors, and stability of metrology components have enabled measurements with a high level of precision (i.e., low three-sigma values), but the ability to track variations of measured parameters through a process window remains elusive.
Reliance on first order analyses of random temporal noise perturbations (e.g., multi-dimensional Taylor series expansions to first order) results in reasonably accurate predictions in a measurement scenario where measurement signal perturbations (random or systematic) are small compared to measurement signal variation due to changes in the measured parameters induced by the manufacturing process. But, if the actual measurement scenario does not comport with this assumption, a first order perturbation analysis may produce erroneous performance predictions. This may occur, for example, in a measurement scenario with low sensitivity and large perturbations, or when multiple perturbations affect the system simultaneously. As a result, a measurement recipe optimized for precision based on a first order perturbation analysis may lead to a metrology tool that reports inaccurate results with seemingly satisfactory precision. This is often evidenced by comparing results of model-based optical measurements and measurements from a trusted reference measurement system such as transmission electron microscope (TEM).
Future metrology applications present challenges for metrology due to increasingly small resolution requirements, multi-parameter correlation, increasingly complex geometric structures, and increasing use of opaque materials. It is becoming more important to track process-induced parameter variations, such as CD or film thickness variations, and the lack of parameter tracking capability is a serious challenge. Thus, methods and systems for improved measurements are desired.